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import numpy as np
import pandas as pd
from PIL import Image
from PIL import ImageFile
import urllib.request
from sklearn.metrics import pairwise_distances
from datetime import datetime
import streamlit as st
st.set_option('deprecation.showfileUploaderEncoding', False)
fashion_df = pd.read_csv("./fashion.csv")
boys_extracted_features = np.load('./Boys_ResNet_features.npy')
boys_Productids = np.load('./Boys_ResNet_feature_product_ids.npy')
girls_extracted_features = np.load('./Girls_ResNet_features.npy')
girls_Productids = np.load('./Girls_ResNet_feature_product_ids.npy')
men_extracted_features = np.load('./Men_ResNet_features.npy')
men_Productids = np.load('./Men_ResNet_feature_product_ids.npy')
women_extracted_features = np.load('./Women_ResNet_features.npy')
women_Productids = np.load('./Women_ResNet_feature_product_ids.npy')
fashion_df["ProductId"] = fashion_df["ProductId"].astype(str)
st.image("https://storage.googleapis.com/danacita-website-v3-prd/website_v3/images/biaya_bootcamp__kursus_hacktiv8_6.original.png")
st.markdown('---')
st.subheader('FashClass - HCK-14 Final Project')
st.write('Name :')
st.write('1. Anjas Fajar Maulana (Data Science)')
st.write('2. Fazrin Muhammad (Data Analyst)')
st.write('3. Naufal Andika Ramadhan (Data Engineer)')
st.write('4. Salsa Sabitha Hurriyah (Data Science)')
st.write('---')
def load_data(file_path):
return pd.read_csv(file_path)
# Path to the CSV file
file_path = "fashion.csv"
# Load the data
data = load_data(file_path)
# Display the data using Streamlit
st.write("### List of Product")
# Create a button to show/hide the data
if st.button("Show Data"):
st.write(data)
st.write('---')
# Define function to filter dataset based on gender
def filter_dataset_by_gender(data, gender_filter):
filtered_data = data[data['Gender'].str.contains(gender_filter, case=False)]
return filtered_data
st.write("### Filter")
# Create a text_input for filtering by gender
gender_filter = st.selectbox("Filter by gender", ["Boys", "Girls", "Men", "Women"])
# Filter the dataset based on the input gender filter
filtered_data = filter_dataset_by_gender(data, gender_filter)
# Display the filtered dataset
st.write(filtered_data)
st.write('---')
def get_similar_products_cnn(product_id, num_results):
if product_id not in fashion_df['ProductId'].values:
st.write("❌ Product ID is not valid")
return
if(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Boys"):
extracted_features = boys_extracted_features
Productids = boys_Productids
elif(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Girls"):
extracted_features = girls_extracted_features
Productids = girls_Productids
elif(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Men"):
extracted_features = men_extracted_features
Productids = men_Productids
elif(fashion_df[fashion_df['ProductId']==product_id]['Gender'].values[0]=="Women"):
extracted_features = women_extracted_features
Productids = women_Productids
Productids = list(Productids)
doc_id = Productids.index(product_id)
pairwise_dist = pairwise_distances(extracted_features, extracted_features[doc_id].reshape(1,-1))
indices = np.argsort(pairwise_dist.flatten())[0:num_results+1]
pdists = np.sort(pairwise_dist.flatten())[0:num_results+1]
st.write("""
#### input item details
""")
ip_row = fashion_df[['ImageURL','ProductTitle']].loc[fashion_df['ProductId']==Productids[indices[0]]]
for indx, row in ip_row.iterrows():
image = Image.open(urllib.request.urlopen(row['ImageURL']))
image = image.resize((224,224))
st.image(image)
st.write(f"Product Title: {row['ProductTitle']}")
st.write(f"""
#### Top {num_results} Recommended items
""")
for i in range(1,len(indices)):
rows = fashion_df[['Gender','ImageURL','ProductTitle','SubCategory']].loc[fashion_df['ProductId']==Productids[indices[i]]]
for indx, row in rows.iterrows():
#image = Image.open(Image(url=row['ImageURL'], width = 224, height = 224,embed=True))
image = Image.open(urllib.request.urlopen(row['ImageURL']))
image = image.resize((224,224))
st.image(image)
st.write(f"Gender Class: {row['Gender']}")
st.write(f"Sub Category: {row['SubCategory']}")
st.write(f"Product Title: {row['ProductTitle']}")
#st.write(f"Euclidean Distance from input image: {pdists[i]}")
st.write("""
## FashClass Recommendation
"""
)
user_input1 = st.text_input("Enter the item id")
user_input2 = st.text_input("Enter number of products to be recommended")
button = st.button('Generate recommendations')
st.write('---')
if button:
get_similar_products_cnn(str(user_input1), int(user_input2)) |